Overview

Dataset statistics

Number of variables21
Number of observations73086
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.4 MiB
Average record size in memory221.0 B

Variable types

Numeric13
DateTime4
Categorical4

Alerts

INITIAL_COST has constant value ""Constant
FINAL_COST has constant value ""Constant
MAIN_SYSTEM_ID is highly overall correlated with RETAILER_IDHigh correlation
RETAILER_ID is highly overall correlated with MAIN_SYSTEM_IDHigh correlation
LOAN_ID is highly overall correlated with REPAYMENT_IDHigh correlation
LOAN_AMOUNT is highly overall correlated with TOTAL_INITIAL_AMOUNT and 3 other fieldsHigh correlation
TOTAL_INITIAL_AMOUNT is highly overall correlated with LOAN_AMOUNT and 3 other fieldsHigh correlation
PAYMENT_AMOUNT is highly overall correlated with LOAN_AMOUNT and 3 other fieldsHigh correlation
FIRST_TRIAL_BALANCE is highly overall correlated with SPENT and 2 other fieldsHigh correlation
SPENT is highly overall correlated with LOAN_AMOUNT and 5 other fieldsHigh correlation
TOTAL_FINAL_AMOUNT is highly overall correlated with LOAN_AMOUNT and 5 other fieldsHigh correlation
REPAYMENT_ID is highly overall correlated with LOAN_IDHigh correlation
CUMMULATIVE_OUTSTANDING is highly overall correlated with FIRST_TRIAL_BALANCE and 2 other fieldsHigh correlation
FIRST_TRAIL_DELAYS is highly imbalanced (74.7%)Imbalance
PAYMENT_STATUS is highly imbalanced (99.9%)Imbalance
REPAYMENT_AMOUNT is highly skewed (γ1 = 68.19202304)Skewed
INDEX has unique valuesUnique
LOAN_ID has unique valuesUnique
PAYMENT_AMOUNT has 766 (1.0%) zerosZeros
SPENT has 15465 (21.2%) zerosZeros
TOTAL_FINAL_AMOUNT has 15465 (21.2%) zerosZeros
REPAYMENT_AMOUNT has 72398 (99.1%) zerosZeros

Reproduction

Analysis started2023-04-04 15:40:14.100490
Analysis finished2023-04-04 15:40:36.628611
Duration22.53 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

INDEX
Real number (ℝ)

Distinct73086
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean321333.57
Minimum10
Maximum644668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size571.1 KiB
2023-04-04T18:40:36.689931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile31178
Q1160733.75
median322029
Q3483033.25
95-th percentile612866
Maximum644668
Range644658
Interquartile range (IQR)322299.5

Descriptive statistics

Standard deviation185948.5
Coefficient of variation (CV)0.57867747
Kurtosis-1.1960144
Mean321333.57
Median Absolute Deviation (MAD)161145.5
Skewness0.001457336
Sum2.3484985 × 1010
Variance3.4576843 × 1010
MonotonicityNot monotonic
2023-04-04T18:40:36.811930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
524291 1
 
< 0.1%
311207 1
 
< 0.1%
311271 1
 
< 0.1%
311268 1
 
< 0.1%
311259 1
 
< 0.1%
311237 1
 
< 0.1%
311232 1
 
< 0.1%
311214 1
 
< 0.1%
311206 1
 
< 0.1%
311287 1
 
< 0.1%
Other values (73076) 73076
> 99.9%
ValueCountFrequency (%)
10 1
< 0.1%
11 1
< 0.1%
17 1
< 0.1%
31 1
< 0.1%
35 1
< 0.1%
39 1
< 0.1%
73 1
< 0.1%
74 1
< 0.1%
81 1
< 0.1%
83 1
< 0.1%
ValueCountFrequency (%)
644668 1
< 0.1%
644667 1
< 0.1%
644666 1
< 0.1%
644665 1
< 0.1%
644654 1
< 0.1%
644650 1
< 0.1%
644648 1
< 0.1%
644647 1
< 0.1%
644646 1
< 0.1%
644633 1
< 0.1%

MAIN_SYSTEM_ID
Real number (ℝ)

Distinct4997
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51831.062
Minimum102
Maximum156199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size571.1 KiB
2023-04-04T18:40:36.943410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile7505
Q120251
median41244
Q375355
95-th percentile127207
Maximum156199
Range156097
Interquartile range (IQR)55104

Descriptive statistics

Standard deviation38218.271
Coefficient of variation (CV)0.7373623
Kurtosis-0.12595415
Mean51831.062
Median Absolute Deviation (MAD)25088
Skewness0.842807
Sum3.788125 × 109
Variance1.4606362 × 109
MonotonicityNot monotonic
2023-04-04T18:40:37.066053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74036 219
 
0.3%
48645 99
 
0.1%
12578 96
 
0.1%
36935 82
 
0.1%
32967 76
 
0.1%
2972 75
 
0.1%
13311 72
 
0.1%
44602 71
 
0.1%
33890 69
 
0.1%
15570 69
 
0.1%
Other values (4987) 72158
98.7%
ValueCountFrequency (%)
102 17
< 0.1%
115 7
 
< 0.1%
122 1
 
< 0.1%
193 1
 
< 0.1%
247 20
< 0.1%
248 5
 
< 0.1%
275 5
 
< 0.1%
290 10
< 0.1%
296 1
 
< 0.1%
300 17
< 0.1%
ValueCountFrequency (%)
156199 3
 
< 0.1%
156063 3
 
< 0.1%
155983 3
 
< 0.1%
155952 11
< 0.1%
155936 1
 
< 0.1%
155917 24
< 0.1%
155894 12
< 0.1%
155803 18
< 0.1%
155673 1
 
< 0.1%
155667 7
 
< 0.1%

RETAILER_ID
Real number (ℝ)

Distinct4997
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51476.708
Minimum64
Maximum833111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size571.1 KiB
2023-04-04T18:40:37.201694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum64
5-th percentile5663
Q118803
median40791
Q375703
95-th percentile103103
Maximum833111
Range833047
Interquartile range (IQR)56900

Descriptive statistics

Standard deviation52757.279
Coefficient of variation (CV)1.0248767
Kurtosis69.087714
Mean51476.708
Median Absolute Deviation (MAD)25286
Skewness6.1235116
Sum3.7622267 × 109
Variance2.7833305 × 109
MonotonicityNot monotonic
2023-04-04T18:40:37.322409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57878 219
 
0.3%
78224 99
 
0.1%
10413 96
 
0.1%
36625 82
 
0.1%
30389 76
 
0.1%
2177 75
 
0.1%
12998 72
 
0.1%
48272 71
 
0.1%
31649 69
 
0.1%
16872 69
 
0.1%
Other values (4987) 72158
98.7%
ValueCountFrequency (%)
64 1
 
< 0.1%
286 10
 
< 0.1%
530 1
 
< 0.1%
560 26
< 0.1%
814 10
 
< 0.1%
815 15
 
< 0.1%
931 11
 
< 0.1%
1051 1
 
< 0.1%
1062 29
< 0.1%
1080 51
0.1%
ValueCountFrequency (%)
833111 1
 
< 0.1%
832397 3
 
< 0.1%
792989 26
< 0.1%
790457 27
< 0.1%
780798 24
< 0.1%
737719 2
 
< 0.1%
709463 17
< 0.1%
664029 17
< 0.1%
629637 1
 
< 0.1%
627912 2
 
< 0.1%

LOAN_ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct73086
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean831119.78
Minimum4917
Maximum964571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size571.1 KiB
2023-04-04T18:40:37.449486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4917
5-th percentile706243
Q1770708
median832068.5
Q3897874.75
95-th percentile951253.75
Maximum964571
Range959654
Interquartile range (IQR)127166.75

Descriptive statistics

Standard deviation82904.81
Coefficient of variation (CV)0.099750737
Kurtosis11.522108
Mean831119.78
Median Absolute Deviation (MAD)64360
Skewness-1.3326786
Sum6.074322 × 1010
Variance6.8732076 × 109
MonotonicityNot monotonic
2023-04-04T18:40:37.578971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
754724 1
 
< 0.1%
715853 1
 
< 0.1%
889286 1
 
< 0.1%
716800 1
 
< 0.1%
716713 1
 
< 0.1%
889098 1
 
< 0.1%
716570 1
 
< 0.1%
716199 1
 
< 0.1%
715852 1
 
< 0.1%
720777 1
 
< 0.1%
Other values (73076) 73076
> 99.9%
ValueCountFrequency (%)
4917 1
< 0.1%
6151 1
< 0.1%
6157 1
< 0.1%
6256 1
< 0.1%
6271 1
< 0.1%
6275 1
< 0.1%
6326 1
< 0.1%
6345 1
< 0.1%
6365 1
< 0.1%
6367 1
< 0.1%
ValueCountFrequency (%)
964571 1
< 0.1%
964570 1
< 0.1%
964566 1
< 0.1%
964564 1
< 0.1%
964563 1
< 0.1%
964562 1
< 0.1%
964561 1
< 0.1%
964557 1
< 0.1%
964556 1
< 0.1%
964554 1
< 0.1%
Distinct73085
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size571.1 KiB
Minimum2021-09-27 15:41:29.692000
Maximum2022-10-31 23:54:35.343000
2023-04-04T18:40:37.703076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:37.819779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

LOAN_AMOUNT
Real number (ℝ)

Distinct136
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1722.0188
Minimum135
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size571.1 KiB
2023-04-04T18:40:37.949831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile1000
Q11000
median1500
Q32000
95-th percentile3000
Maximum20000
Range19865
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation897.69607
Coefficient of variation (CV)0.52130445
Kurtosis15.029727
Mean1722.0188
Median Absolute Deviation (MAD)500
Skewness2.1050342
Sum1.2585547 × 108
Variance805858.23
MonotonicityNot monotonic
2023-04-04T18:40:38.082429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 28068
38.4%
1500 16000
21.9%
3000 11680
16.0%
2000 8911
 
12.2%
2500 2476
 
3.4%
1200 992
 
1.4%
500 642
 
0.9%
4000 616
 
0.8%
3500 543
 
0.7%
5000 529
 
0.7%
Other values (126) 2629
 
3.6%
ValueCountFrequency (%)
135 1
 
< 0.1%
300 2
 
< 0.1%
359 1
 
< 0.1%
500 642
0.9%
548.81 1
 
< 0.1%
550 2
 
< 0.1%
600 30
 
< 0.1%
647 1
 
< 0.1%
690 1
 
< 0.1%
694.88 1
 
< 0.1%
ValueCountFrequency (%)
20000 2
 
< 0.1%
19000 1
 
< 0.1%
15000 6
 
< 0.1%
12728 1
 
< 0.1%
12000 2
 
< 0.1%
10500 1
 
< 0.1%
10000 18
< 0.1%
8500 14
< 0.1%
8000 15
< 0.1%
7500 7
 
< 0.1%

INITIAL_COST
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
73086 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73086
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73086
100.0%

Length

2023-04-04T18:40:38.199854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-04T18:40:38.297004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 73086
100.0%

Most occurring characters

ValueCountFrequency (%)
0 73086
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 73086
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 73086
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 73086
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 73086
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 73086
100.0%

TOTAL_INITIAL_AMOUNT
Real number (ℝ)

Distinct136
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1722.0188
Minimum135
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size571.1 KiB
2023-04-04T18:40:38.562839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile1000
Q11000
median1500
Q32000
95-th percentile3000
Maximum20000
Range19865
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation897.69607
Coefficient of variation (CV)0.52130445
Kurtosis15.029727
Mean1722.0188
Median Absolute Deviation (MAD)500
Skewness2.1050342
Sum1.2585547 × 108
Variance805858.23
MonotonicityNot monotonic
2023-04-04T18:40:38.689008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 28068
38.4%
1500 16000
21.9%
3000 11680
16.0%
2000 8911
 
12.2%
2500 2476
 
3.4%
1200 992
 
1.4%
500 642
 
0.9%
4000 616
 
0.8%
3500 543
 
0.7%
5000 529
 
0.7%
Other values (126) 2629
 
3.6%
ValueCountFrequency (%)
135 1
 
< 0.1%
300 2
 
< 0.1%
359 1
 
< 0.1%
500 642
0.9%
548.81 1
 
< 0.1%
550 2
 
< 0.1%
600 30
 
< 0.1%
647 1
 
< 0.1%
690 1
 
< 0.1%
694.88 1
 
< 0.1%
ValueCountFrequency (%)
20000 2
 
< 0.1%
19000 1
 
< 0.1%
15000 6
 
< 0.1%
12728 1
 
< 0.1%
12000 2
 
< 0.1%
10500 1
 
< 0.1%
10000 18
< 0.1%
8500 14
< 0.1%
8000 15
< 0.1%
7500 7
 
< 0.1%
Distinct88
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size571.1 KiB
Minimum2021-09-27 00:00:00
Maximum2022-11-02 00:00:00
2023-04-04T18:40:38.820748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:38.937584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct72551
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size571.1 KiB
Minimum2021-10-06 11:51:38.334000
Maximum2022-11-03 16:53:22.774000
2023-04-04T18:40:39.061790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:39.178267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PAYMENT_AMOUNT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct286
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1867.8091
Minimum0
Maximum47000
Zeros766
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size571.1 KiB
2023-04-04T18:40:39.303767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1000
Q11000
median1500
Q32500
95-th percentile3750
Maximum47000
Range47000
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation1150.8299
Coefficient of variation (CV)0.61613892
Kurtosis56.13891
Mean1867.8091
Median Absolute Deviation (MAD)500
Skewness3.5970973
Sum1.3651069 × 108
Variance1324409.4
MonotonicityNot monotonic
2023-04-04T18:40:39.421592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 17395
23.8%
1500 11631
15.9%
3000 8270
11.3%
2000 7710
10.5%
1100 2606
 
3.6%
2500 2201
 
3.0%
1050 1973
 
2.7%
1200 1751
 
2.4%
1600 1733
 
2.4%
3100 1445
 
2.0%
Other values (276) 16371
22.4%
ValueCountFrequency (%)
0 766
1.0%
0.16 1
 
< 0.1%
50 2
 
< 0.1%
100 12
 
< 0.1%
150 2
 
< 0.1%
200 12
 
< 0.1%
250 6
 
< 0.1%
300 137
 
0.2%
320 1
 
< 0.1%
350 12
 
< 0.1%
ValueCountFrequency (%)
47000 1
 
< 0.1%
30000 1
 
< 0.1%
29500 1
 
< 0.1%
20000 6
< 0.1%
19000 1
 
< 0.1%
18000 1
 
< 0.1%
15600 1
 
< 0.1%
15500 1
 
< 0.1%
15000 3
< 0.1%
13500 1
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
67277 
1
 
5808
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73086
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 67277
92.1%
1 5808
 
7.9%
2 1
 
< 0.1%

Length

2023-04-04T18:40:39.525184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-04T18:40:39.626705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 67277
92.1%
1 5808
 
7.9%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 67277
92.1%
1 5808
 
7.9%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 73086
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 67277
92.1%
1 5808
 
7.9%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 73086
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 67277
92.1%
1 5808
 
7.9%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 67277
92.1%
1 5808
 
7.9%
2 1
 
< 0.1%

FIRST_TRIAL_BALANCE
Real number (ℝ)

Distinct58978
Distinct (%)80.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1113.6922
Minimum-14324.51
Maximum40269.9
Zeros105
Zeros (%)0.1%
Negative695
Negative (%)1.0%
Memory size571.1 KiB
2023-04-04T18:40:39.725788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-14324.51
5-th percentile3.4225
Q1335.2575
median906.28
Q31374.9275
95-th percentile3116.2025
Maximum40269.9
Range54594.41
Interquartile range (IQR)1039.67

Descriptive statistics

Standard deviation1430.9159
Coefficient of variation (CV)1.2848396
Kurtosis60.525786
Mean1113.6922
Median Absolute Deviation (MAD)524.62
Skewness5.3509308
Sum81395312
Variance2047520.3
MonotonicityNot monotonic
2023-04-04T18:40:39.838041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 105
 
0.1%
0.66 26
 
< 0.1%
0.67 25
 
< 0.1%
0.36 23
 
< 0.1%
0.97 21
 
< 0.1%
1.06 21
 
< 0.1%
0.24 21
 
< 0.1%
0.41 20
 
< 0.1%
1.35 19
 
< 0.1%
0.81 19
 
< 0.1%
Other values (58968) 72786
99.6%
ValueCountFrequency (%)
-14324.51 1
< 0.1%
-11953.44 1
< 0.1%
-4413.4 1
< 0.1%
-4408.37 1
< 0.1%
-3999.12 1
< 0.1%
-3995.89 1
< 0.1%
-3991.98 2
< 0.1%
-3863.15 1
< 0.1%
-3750.19 1
< 0.1%
-3591.34 1
< 0.1%
ValueCountFrequency (%)
40269.9 1
< 0.1%
35851.45 2
< 0.1%
31078.73 1
< 0.1%
30178.21 1
< 0.1%
30138.72 1
< 0.1%
25770.39 1
< 0.1%
23706.6 1
< 0.1%
23478.21 1
< 0.1%
23039.55 1
< 0.1%
22525.77 1
< 0.1%

SPENT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47382
Distinct (%)64.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean893.89495
Minimum0
Maximum19882.51
Zeros15465
Zeros (%)21.2%
Negative0
Negative (%)0.0%
Memory size571.1 KiB
2023-04-04T18:40:39.963461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q148.26
median582.775
Q31402.7175
95-th percentile2990.7713
Maximum19882.51
Range19882.51
Interquartile range (IQR)1354.4575

Descriptive statistics

Standard deviation993.12137
Coefficient of variation (CV)1.1110046
Kurtosis5.4574873
Mean893.89495
Median Absolute Deviation (MAD)582.775
Skewness1.5548884
Sum65331206
Variance986290.06
MonotonicityNot monotonic
2023-04-04T18:40:40.090438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15465
 
21.2%
2999.33 17
 
< 0.1%
2999.37 16
 
< 0.1%
2999.15 15
 
< 0.1%
2998.65 15
 
< 0.1%
999.34 14
 
< 0.1%
2998.94 14
 
< 0.1%
2999.11 13
 
< 0.1%
2999.59 13
 
< 0.1%
2999.65 12
 
< 0.1%
Other values (47372) 57492
78.7%
ValueCountFrequency (%)
0 15465
21.2%
0.04 1
 
< 0.1%
0.05 1
 
< 0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.1 1
 
< 0.1%
0.11 1
 
< 0.1%
0.12 2
 
< 0.1%
0.13 1
 
< 0.1%
0.14 1
 
< 0.1%
ValueCountFrequency (%)
19882.51 1
< 0.1%
14999.88 1
< 0.1%
14324.51 1
< 0.1%
11953.44 1
< 0.1%
11148.55 1
< 0.1%
10116.03 1
< 0.1%
9998.99 1
< 0.1%
9998.6 1
< 0.1%
9996.2 1
< 0.1%
9972.42 1
< 0.1%

FINAL_COST
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
73086 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73086
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73086
100.0%

Length

2023-04-04T18:40:40.207608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-04T18:40:40.304530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 73086
100.0%

Most occurring characters

ValueCountFrequency (%)
0 73086
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 73086
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 73086
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 73086
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 73086
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 73086
100.0%

TOTAL_FINAL_AMOUNT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47382
Distinct (%)64.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean893.89495
Minimum0
Maximum19882.51
Zeros15465
Zeros (%)21.2%
Negative0
Negative (%)0.0%
Memory size571.1 KiB
2023-04-04T18:40:40.399904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q148.26
median582.775
Q31402.7175
95-th percentile2990.7713
Maximum19882.51
Range19882.51
Interquartile range (IQR)1354.4575

Descriptive statistics

Standard deviation993.12137
Coefficient of variation (CV)1.1110046
Kurtosis5.4574873
Mean893.89495
Median Absolute Deviation (MAD)582.775
Skewness1.5548884
Sum65331206
Variance986290.06
MonotonicityNot monotonic
2023-04-04T18:40:40.527584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15465
 
21.2%
2999.33 17
 
< 0.1%
2999.37 16
 
< 0.1%
2999.15 15
 
< 0.1%
2998.65 15
 
< 0.1%
999.34 14
 
< 0.1%
2998.94 14
 
< 0.1%
2999.11 13
 
< 0.1%
2999.59 13
 
< 0.1%
2999.65 12
 
< 0.1%
Other values (47372) 57492
78.7%
ValueCountFrequency (%)
0 15465
21.2%
0.04 1
 
< 0.1%
0.05 1
 
< 0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.1 1
 
< 0.1%
0.11 1
 
< 0.1%
0.12 2
 
< 0.1%
0.13 1
 
< 0.1%
0.14 1
 
< 0.1%
ValueCountFrequency (%)
19882.51 1
< 0.1%
14999.88 1
< 0.1%
14324.51 1
< 0.1%
11953.44 1
< 0.1%
11148.55 1
< 0.1%
10116.03 1
< 0.1%
9998.99 1
< 0.1%
9998.6 1
< 0.1%
9996.2 1
< 0.1%
9972.42 1
< 0.1%

REPAYMENT_ID
Real number (ℝ)

Distinct73021
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean831355.02
Minimum4917
Maximum1520356
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size571.1 KiB
2023-04-04T18:40:40.663571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4917
5-th percentile706349.75
Q1770823
median832201.5
Q3897996.75
95-th percentile951407.5
Maximum1520356
Range1515439
Interquartile range (IQR)127173.75

Descriptive statistics

Standard deviation83170.353
Coefficient of variation (CV)0.10004192
Kurtosis11.578458
Mean831355.02
Median Absolute Deviation (MAD)64387.5
Skewness-1.2877125
Sum6.0760413 × 1010
Variance6.9173076 × 109
MonotonicityNot monotonic
2023-04-04T18:40:40.787945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
924749 3
 
< 0.1%
885753 2
 
< 0.1%
936087 2
 
< 0.1%
936242 2
 
< 0.1%
925768 2
 
< 0.1%
905030 2
 
< 0.1%
819766 2
 
< 0.1%
948812 2
 
< 0.1%
948444 2
 
< 0.1%
917815 2
 
< 0.1%
Other values (73011) 73065
> 99.9%
ValueCountFrequency (%)
4917 1
< 0.1%
6151 1
< 0.1%
6157 1
< 0.1%
6256 1
< 0.1%
6271 1
< 0.1%
6275 1
< 0.1%
6326 1
< 0.1%
6345 1
< 0.1%
6365 1
< 0.1%
6367 1
< 0.1%
ValueCountFrequency (%)
1520356 1
< 0.1%
1455996 1
< 0.1%
1378520 1
< 0.1%
1345681 1
< 0.1%
1306725 1
< 0.1%
1280781 1
< 0.1%
1272046 1
< 0.1%
1255607 1
< 0.1%
1210023 1
< 0.1%
1187193 1
< 0.1%

REPAYMENT_AMOUNT
Real number (ℝ)

SKEWED  ZEROS 

Distinct193
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.561667
Minimum-2.49
Maximum35283
Zeros72398
Zeros (%)99.1%
Negative1
Negative (%)< 0.1%
Memory size571.1 KiB
2023-04-04T18:40:40.920684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2.49
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum35283
Range35285.49
Interquartile range (IQR)0

Descriptive statistics

Standard deviation219.68641
Coefficient of variation (CV)16.199072
Kurtosis9436.309
Mean13.561667
Median Absolute Deviation (MAD)0
Skewness68.192023
Sum991167.96
Variance48262.121
MonotonicityNot monotonic
2023-04-04T18:40:41.043664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 72398
99.1%
1000 104
 
0.1%
1500 59
 
0.1%
2000 54
 
0.1%
3000 50
 
0.1%
500 35
 
< 0.1%
2500 17
 
< 0.1%
300 15
 
< 0.1%
100 14
 
< 0.1%
1200 14
 
< 0.1%
Other values (183) 326
 
0.4%
ValueCountFrequency (%)
-2.49 1
 
< 0.1%
0 72398
99.1%
3 1
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
6.68 1
 
< 0.1%
10 2
 
< 0.1%
15 1
 
< 0.1%
20 3
 
< 0.1%
30 1
 
< 0.1%
ValueCountFrequency (%)
35283 1
< 0.1%
13000 1
< 0.1%
10000 1
< 0.1%
7500 1
< 0.1%
6440 1
< 0.1%
6320 1
< 0.1%
5020 1
< 0.1%
5000 1
< 0.1%
4515 1
< 0.1%
4500 2
< 0.1%
Distinct72534
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size571.1 KiB
Minimum2021-10-06 11:51:38.334000
Maximum2023-02-09 17:30:18.093000
2023-04-04T18:40:41.170486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:41.284792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

CUMMULATIVE_OUTSTANDING
Real number (ℝ)

Distinct58902
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1128.3532
Minimum-3750.19
Maximum40269.9
Zeros107
Zeros (%)0.1%
Negative9
Negative (%)< 0.1%
Memory size571.1 KiB
2023-04-04T18:40:41.402970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-3750.19
5-th percentile4.2225
Q1341.1425
median907.815
Q31376.0225
95-th percentile3117.7775
Maximum40269.9
Range44020.09
Interquartile range (IQR)1034.88

Descriptive statistics

Standard deviation1416.3257
Coefficient of variation (CV)1.2552149
Kurtosis65.802325
Mean1128.3532
Median Absolute Deviation (MAD)522.31
Skewness5.7290155
Sum82466821
Variance2005978.6
MonotonicityNot monotonic
2023-04-04T18:40:41.522126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 107
 
0.1%
0.66 26
 
< 0.1%
0.67 25
 
< 0.1%
0.36 23
 
< 0.1%
0.97 21
 
< 0.1%
1.06 21
 
< 0.1%
0.24 21
 
< 0.1%
0.41 20
 
< 0.1%
1.35 19
 
< 0.1%
0.81 19
 
< 0.1%
Other values (58892) 72784
99.6%
ValueCountFrequency (%)
-3750.19 1
 
< 0.1%
-2942.85 1
 
< 0.1%
-2498.01 1
 
< 0.1%
-1496.84 1
 
< 0.1%
-1195.51 1
 
< 0.1%
-1000.91 1
 
< 0.1%
-792.56 1
 
< 0.1%
-738.62 1
 
< 0.1%
-470.2 1
 
< 0.1%
0 107
0.1%
ValueCountFrequency (%)
40269.9 1
< 0.1%
35851.45 2
< 0.1%
32483.78 1
< 0.1%
31078.73 1
< 0.1%
30178.21 1
< 0.1%
30138.72 1
< 0.1%
25770.39 1
< 0.1%
23706.6 1
< 0.1%
23478.21 1
< 0.1%
23039.55 1
< 0.1%

PAYMENT_STATUS
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
Paid
73077 
Unpaid
 
6
Partialy paid
 
3

Length

Max length13
Median length4
Mean length4.0005336
Min length4

Characters and Unicode

Total characters292383
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPaid
2nd rowPaid
3rd rowPaid
4th rowPaid
5th rowPaid

Common Values

ValueCountFrequency (%)
Paid 73077
> 99.9%
Unpaid 6
 
< 0.1%
Partialy paid 3
 
< 0.1%

Length

2023-04-04T18:40:41.627909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-04T18:40:41.731610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
paid 73080
> 99.9%
unpaid 6
 
< 0.1%
partialy 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 73092
25.0%
i 73089
25.0%
d 73086
25.0%
P 73080
25.0%
p 9
 
< 0.1%
U 6
 
< 0.1%
n 6
 
< 0.1%
r 3
 
< 0.1%
t 3
 
< 0.1%
l 3
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 219294
75.0%
Uppercase Letter 73086
 
25.0%
Space Separator 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 73092
33.3%
i 73089
33.3%
d 73086
33.3%
p 9
 
< 0.1%
n 6
 
< 0.1%
r 3
 
< 0.1%
t 3
 
< 0.1%
l 3
 
< 0.1%
y 3
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
P 73080
> 99.9%
U 6
 
< 0.1%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 292380
> 99.9%
Common 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 73092
25.0%
i 73089
25.0%
d 73086
25.0%
P 73080
25.0%
p 9
 
< 0.1%
U 6
 
< 0.1%
n 6
 
< 0.1%
r 3
 
< 0.1%
t 3
 
< 0.1%
l 3
 
< 0.1%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 292383
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 73092
25.0%
i 73089
25.0%
d 73086
25.0%
P 73080
25.0%
p 9
 
< 0.1%
U 6
 
< 0.1%
n 6
 
< 0.1%
r 3
 
< 0.1%
t 3
 
< 0.1%
l 3
 
< 0.1%
Other values (2) 6
 
< 0.1%

Interactions

2023-04-04T18:40:34.607103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:17.158317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:18.563560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:20.071893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:21.543988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:22.912226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:24.354700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:25.814995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:27.155858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:28.700039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:30.144886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:31.606042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:33.029624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:34.710438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:17.264078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:18.678056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:20.172000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:21.648160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:23.020932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:24.466193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:25.917418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:27.261878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:28.810074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:30.255848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:31.715014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:33.136863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:34.819819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:17.383987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:18.799002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:20.284467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:21.759125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:23.139761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:24.584881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:26.026378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:27.375610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:28.929014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:30.373509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:31.830929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:33.253905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:34.914320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:17.483677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:18.904413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:20.376072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:21.854675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:23.242443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:24.688583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:26.119252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:27.637602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:29.031727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:30.476642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:31.931846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:33.354236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:35.013912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:17.589198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:19.016609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:20.475696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:21.953556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:23.349173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:24.797113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:26.219633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:27.741101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:29.139221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:30.585026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:32.035802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:33.459635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:35.124510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:17.702068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:19.150890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:20.583925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:22.065080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:23.465482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:24.917505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:26.328294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:27.854274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:29.256002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:30.702618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:32.152125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:33.574638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:35.233886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:17.816337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:19.276514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:20.696913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:22.179357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:23.581155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:25.033988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:26.438488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:27.966072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:29.371665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:30.822866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:32.267939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:33.850900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:35.327879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:17.914770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:19.382707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:20.789418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:22.275656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:23.683334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:25.137064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:26.530147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:28.061901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:29.476404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:30.931047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:32.368028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:33.950277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:35.425460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:18.015998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:19.495259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:20.887607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:22.374912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:23.791512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:25.243127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:26.628596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:28.160700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:29.581444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:31.037346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:32.471679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:34.053645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:35.535535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:18.130073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:19.621325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:20.995156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:22.487041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:23.907145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:25.359050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:26.737344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:28.272984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:29.697521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:31.156989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:32.587804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:34.168614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:35.645163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:18.243898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:19.739506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:21.105330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:22.598334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:24.021653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:25.477869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:26.847812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:28.385469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:29.814017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:31.273533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:32.703194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:34.283889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:35.753546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:18.356745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:19.855694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:21.344865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:22.707725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:24.138213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:25.595999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:26.955235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:28.495023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:29.929354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:31.389429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:32.816334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:34.396539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:35.859226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:18.464679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:19.968945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:21.450714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:22.814786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:24.251511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:25.710576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:27.061928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:28.604521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:30.043256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:31.504009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:32.927849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T18:40:34.507379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-04T18:40:41.997101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
INDEXMAIN_SYSTEM_IDRETAILER_IDLOAN_IDLOAN_AMOUNTTOTAL_INITIAL_AMOUNTPAYMENT_AMOUNTFIRST_TRIAL_BALANCESPENTTOTAL_FINAL_AMOUNTREPAYMENT_IDREPAYMENT_AMOUNTCUMMULATIVE_OUTSTANDINGFIRST_TRAIL_DELAYSPAYMENT_STATUS
INDEX1.000-0.004-0.005-0.0060.0010.001-0.003-0.0010.0000.000-0.0060.004-0.0010.0000.000
MAIN_SYSTEM_ID-0.0041.0000.9580.013-0.109-0.109-0.0980.020-0.089-0.0890.0130.0030.0210.0140.013
RETAILER_ID-0.0050.9581.0000.013-0.097-0.097-0.0880.013-0.079-0.0790.0130.0050.0140.0160.000
LOAN_ID-0.0060.0130.0131.0000.0120.0120.0100.015-0.005-0.0050.999-0.0080.0150.0180.028
LOAN_AMOUNT0.001-0.109-0.0970.0121.0001.0000.8850.0440.6110.6110.0120.0120.0440.0180.000
TOTAL_INITIAL_AMOUNT0.001-0.109-0.0970.0121.0001.0000.8850.0440.6110.6110.0120.0120.0440.0180.000
PAYMENT_AMOUNT-0.003-0.098-0.0880.0100.8850.8851.0000.2120.5340.5340.006-0.1510.2030.0100.000
FIRST_TRIAL_BALANCE-0.0010.0200.0130.0150.0440.0440.2121.000-0.607-0.6070.012-0.1670.9960.0560.079
SPENT0.000-0.089-0.079-0.0050.6110.6110.534-0.6071.0001.000-0.0040.043-0.6090.0520.005
TOTAL_FINAL_AMOUNT0.000-0.089-0.079-0.0050.6110.6110.534-0.6071.0001.000-0.0040.043-0.6090.0520.005
REPAYMENT_ID-0.0060.0130.0130.9990.0120.0120.0060.012-0.004-0.0041.0000.0110.0120.0320.369
REPAYMENT_AMOUNT0.0040.0030.005-0.0080.0120.012-0.151-0.1670.0430.0430.0111.000-0.1060.0360.000
CUMMULATIVE_OUTSTANDING-0.0010.0210.0140.0150.0440.0440.2030.996-0.609-0.6090.012-0.1061.0000.1040.000
FIRST_TRAIL_DELAYS0.0000.0140.0160.0180.0180.0180.0100.0560.0520.0520.0320.0360.1041.0000.023
PAYMENT_STATUS0.0000.0130.0000.0280.0000.0000.0000.0790.0050.0050.3690.0000.0000.0231.000

Missing values

2023-04-04T18:40:36.026470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-04T18:40:36.376485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

INDEXMAIN_SYSTEM_IDRETAILER_IDLOAN_IDLOAN_ISSUANCE_DATELOAN_AMOUNTINITIAL_COSTTOTAL_INITIAL_AMOUNTINITIAL_DATELOAN_PAYMENT_DATEPAYMENT_AMOUNTFIRST_TRAIL_DELAYSFIRST_TRIAL_BALANCESPENTFINAL_COSTTOTAL_FINAL_AMOUNTREPAYMENT_IDREPAYMENT_AMOUNTREPAYMENT_UPDATEDCUMMULATIVE_OUTSTANDINGPAYMENT_STATUS
052429135668340197547242022-09-13 07:58:24.2921000.001000.02022-09-132022-09-13 16:39:44.1271020.00975.3944.61044.617547240.02022-09-13 16:39:44.127975.39Paid
152429225120282327547702022-09-13 08:24:02.8243000.003000.02022-09-142022-09-14 11:28:18.8873000.002.172997.8302997.837547700.02022-09-14 11:28:18.8872.17Paid
252429320838231567548142022-09-13 08:47:08.0211500.001500.02022-09-132022-09-14 19:49:44.2831500.0121.151478.8501478.857548140.02022-09-14 19:49:44.28321.15Paid
3524321708050777550412022-09-13 09:39:29.5771000.001000.02022-09-132022-09-13 10:35:35.9061100.001102.010.0000.007550410.02022-09-13 10:35:35.9061102.01Paid
452432224892214137550762022-09-13 09:52:06.6802000.002000.02022-09-142022-09-14 12:16:17.2792000.00890.021109.9801109.987550760.02022-09-14 12:16:17.279890.02Paid
552432330739292537550892022-09-13 09:56:28.4171000.001000.02022-09-132022-09-13 12:29:49.0861100.001095.434.5704.577550890.02022-09-13 12:29:49.0861095.43Paid
652433138767363989097942022-10-19 19:09:08.5102000.002000.02022-10-202022-10-20 14:00:31.3992000.00171.781828.2201828.229097940.02022-10-20 14:00:31.399171.78Paid
752433235241371659099262022-10-19 20:02:32.1911500.001500.02022-10-202022-10-20 09:39:53.7971500.001459.9240.08040.089099260.02022-10-20 09:39:53.7971459.92Paid
852433530612999102242022-10-19 22:14:15.6201500.001500.02022-10-212022-10-21 11:09:08.6231500.001269.72230.280230.289102240.02022-10-21 11:09:08.6231269.72Paid
952433640107539069102772022-10-19 22:28:33.1751500.001500.02022-10-202022-10-20 11:16:49.7241000.00645.42354.580354.589102770.02022-10-20 11:16:49.724645.42Paid
INDEXMAIN_SYSTEM_IDRETAILER_IDLOAN_IDLOAN_ISSUANCE_DATELOAN_AMOUNTINITIAL_COSTTOTAL_INITIAL_AMOUNTINITIAL_DATELOAN_PAYMENT_DATEPAYMENT_AMOUNTFIRST_TRAIL_DELAYSFIRST_TRIAL_BALANCESPENTFINAL_COSTTOTAL_FINAL_AMOUNTREPAYMENT_IDREPAYMENT_AMOUNTREPAYMENT_UPDATEDCUMMULATIVE_OUTSTANDINGPAYMENT_STATUS
7307626205644417541659026162022-10-19 05:12:53.2013000.003000.02022-10-202022-10-20 14:28:46.2383000.000.402999.6002999.609026160.02022-10-20 14:28:46.2380.40Paid
73077262057148931939409026392022-10-19 05:35:13.3403000.003000.02022-10-202022-10-20 15:01:56.4253000.001167.931832.0701832.079026390.02022-10-20 15:01:56.4251167.93Paid
7307826206162409651647534632022-09-12 17:56:13.0201500.001500.02022-09-122022-09-12 19:35:45.1071500.001338.84161.160161.167534630.02022-09-12 19:35:45.1071338.84Paid
7307926206729363283649030832022-10-19 08:42:30.3593000.003000.02022-10-202022-10-20 16:07:59.3363000.00203.622796.3802796.389030830.02022-10-20 16:07:59.336203.62Paid
7308026208760590605597538262022-09-12 19:40:03.5662500.002500.02022-09-132022-09-13 19:25:07.6461000.0199.34900.660900.667538260.02022-09-13 19:25:07.64699.34Paid
7308126209076433690867538632022-09-12 19:51:28.6841000.001000.02022-09-132022-09-13 16:19:38.8941000.00566.18433.820433.827538630.02022-09-13 16:19:38.894566.18Paid
7308226209466308866667539572022-09-12 20:34:54.3791000.001000.02022-09-132022-09-13 20:03:52.7871000.00336.71663.290663.297539570.02022-09-13 20:03:52.787336.71Paid
730832620961252284867540262022-09-12 21:16:55.8931500.001500.02022-09-132022-09-13 12:51:57.9961600.00479.381120.6201120.627540260.02022-09-13 12:51:57.996479.38Paid
730842620981173252160917541602022-09-12 22:06:32.1863000.003000.02022-09-142022-09-14 08:51:45.4373200.00205.292994.7102994.717541600.02022-09-14 08:51:45.437205.29Paid
73085262133149207915159051542022-10-19 11:30:45.5221500.001500.02022-10-202022-10-20 17:11:11.5711500.00964.00536.000536.009051540.02022-10-20 17:11:11.571964.00Paid